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Record W2245874545

RIGHT TURN ON RED (RTOR)--ENCOURAGING RESULTS FOR THE GERMAN SOLUTION (ABSTRACT ONLY)

2000· article· en· W2245874545 on OpenAlexaboutno aff
E Brühning

Bibliographic record

VenueTraffic Safety on Two ContinentsPTRC Education and Research Services Limited · 2000
Typearticle
Languageen
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsnot available
Fundersnot available
KeywordsArrowGermanEngineeringUnificationOffset (computer science)TelecommunicationsTransport engineeringOperations managementComputer scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Right Turn On Red (RTOR) is standard practice in the USA and Canada; until the 70s there were, however, two different solutions with differing levels of safety. RTOR has not met with approval from many sides in Europe; here it is generally not permitted to turn right on red. Subsequent to German re-unification, the green arrow contained in the GDRs Road Traffic Regulations came into force in the whole of Germany, initially on a trial basis. The green arrow permits RTOR; contrary to the practice in the USA, however, drivers must stop before turning right and of course respect pedestrians and traffic with the right-of-way. RTOR with a green arrow is common in eastern Germany but is still rare in western Germany. Since 1991 BAST carried out a series of research projects on the subject of RTOR when the green arrow is showing. The focal points of these projects constituted investigations into traffic safety (conflicts and accidents). However, they also dealt with issues connected with traffic flow and operation. The results are encouraging: the advantages relating to efficiency and emissions do not seem to be offset by disadvantages in safety. A panel of German experts is to complete a report by the summer of 1999 which will also concern recommendations for the future use of the green arrow. A considerable increase in the use of RTOR in Germany would trigger an international discussion in Europe and could be a starting point.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.392
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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